/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package com.zhaohg.spark.examples.mllib;

// $example on$

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaDoubleRDD;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.IsotonicRegression;
import org.apache.spark.mllib.regression.IsotonicRegressionModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;
import scala.Tuple3;

// $example off$

public class JavaIsotonicRegressionExample {
    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setAppName("JavaIsotonicRegressionExample");
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        // $example on$
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(
                jsc.sc(), "data/mllib/sample_isotonic_regression_libsvm_data.txt").toJavaRDD();
        
        // Create label, feature, weight tuples from input data with weight set to default value 1.0.
        JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map(
                new Function<LabeledPoint, Tuple3<Double, Double, Double>>() {
                    public Tuple3<Double, Double, Double> call(LabeledPoint point) {
                        return new Tuple3<>(new Double(point.label()),
                                new Double(point.features().apply(0)), 1.0);
                    }
                }
        );
        
        // Split data into training (60%) and demo3 (40%) sets.
        JavaRDD<Tuple3<Double, Double, Double>>[] splits =
                parsedData.randomSplit(new double[]{0.6, 0.4}, 11L);
        JavaRDD<Tuple3<Double, Double, Double>> training = splits[0];
        JavaRDD<Tuple3<Double, Double, Double>> test = splits[1];
        
        // Create isotonic regression model from training data.
        // Isotonic parameter defaults to true so it is only shown for demonstration
        final IsotonicRegressionModel model =
                new IsotonicRegression().setIsotonic(true).run(training);
        
        // Create tuples of predicted and real labels.
        JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair(
                new PairFunction<Tuple3<Double, Double, Double>, Double, Double>() {
                    @Override
                    public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> point) {
                        Double predictedLabel = model.predict(point._2());
                        return new Tuple2<>(predictedLabel, point._1());
                    }
                }
        );
        
        // Calculate mean squared error between predicted and real labels.
        Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map(
                new Function<Tuple2<Double, Double>, Object>() {
                    @Override
                    public Object call(Tuple2<Double, Double> pl) {
                        return Math.pow(pl._1() - pl._2(), 2);
                    }
                }
        ).rdd()).mean();
        System.out.println("Mean Squared Error = " + meanSquaredError);
        
        // Save and load model
        model.save(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
        IsotonicRegressionModel sameModel =
                IsotonicRegressionModel.load(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
        // $example off$
        
        jsc.stop();
    }
}
